Purpose - The aim of this article is to provide a concise methodology for the design of a widely used class of decision supply systems (DSS) which will enable precise control of bullwhip variance and inventory variance induced within a supply chain echelon. Design/methodology/approach - The study exploits recent research that derived analytical formulae for calculating these performance metrics germane to the delivery process when the demand is randomly varying about a constant mean value. These formulae have been verified via extensive simulation-based cross-checks. Findings - The design methodology focuses on the specification of bullwhip variance as an input. The output is to identify combinations of parameter settings to meet this target. Hence these parameters may be mapped to provide a visual display of competing designs with their associated inventory variance. Research limitations/implications - Although the analytical solutions apply only to the case where the pipeline error and inventory error correction terms are equal, this is not a severe limitation. Both theoretical studies of dynamic response and industrial experience support this feedback gain equally as enabling good practice. Practical implications - Design of this particular DSS to control bullwhip is now greatly simplified, and guaranteed via extensive verification tests. The formulae are equally sound as a means of establishing system robustness. Originality/value - The methodology is unique in enabling transparency of both bullwhip variance and inventory variance computation. Not only are system design time saved and normal performance guaranteed, but considerable management insight is generated thereby.